Distribution-Free Fair Federated Learning with Small Samples

arXiv:2402.16158v1 Announce Type: cross
Abstract: As federated learning gains increasing importance in real-world applications due to its capacity for decentralized data training, addressing fairness concerns across demographic groups becomes critically important. However, most existing machine learning algorithms for ensuring fairness are designed for centralized data environments and generally require large-sample and distributional assumptions, underscoring the urgent need for fairness techniques adapted for decentralized and heterogeneous systems with finite-sample and distribution-free guarantees. To address this issue, this paper introduces FedFaiREE, a …

abstract algorithms applications arxiv assumptions capacity centralized data concerns cs.cy cs.lg data decentralized decentralized data distribution environments fair fairness federated learning free importance machine machine learning machine learning algorithms sample samples small stat.ml training type world